Create app.py

app.py

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+import requests
+import torch
+from PIL import Image
+from torchvision import transforms
+
+import gradio as gr
+
+model = torch.hub.load("pytorch/vision:v0.6.0", "resnet18", pretrained=True).eval()
+
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+
+def predict(inp):
+    inp = Image.fromarray(inp.astype("uint8"), "RGB")
+    inp = transforms.ToTensor()(inp).unsqueeze(0)
+    with torch.no_grad():
+        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+    return {labels[i]: float(prediction[i]) for i in range(1000)}
+
+
+inputs = gr.Image()
+outputs = gr.Label(num_top_classes=3)
+
+demo = gr.Interface(fn=predict, inputs=inputs, outputs=outputs)
+
+if __name__ == "__main__":
+    demo.launch()